import matplotlib as plt
import pandas as pd
import seaborn as sns
tips = sns.load_dataset("tips")
tips
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 239 | 29.03 | 5.92 | Male | No | Sat | Dinner | 3 |
| 240 | 27.18 | 2.00 | Female | Yes | Sat | Dinner | 2 |
| 241 | 22.67 | 2.00 | Male | Yes | Sat | Dinner | 2 |
| 242 | 17.82 | 1.75 | Male | No | Sat | Dinner | 2 |
| 243 | 18.78 | 3.00 | Female | No | Thur | Dinner | 2 |
244 rows × 7 columns
penguins = sns.load_dataset("penguins")
penguins
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | |
|---|---|---|---|---|---|---|---|
| 0 | Adelie | Torgersen | 39.1 | 18.7 | 181.0 | 3750.0 | Male |
| 1 | Adelie | Torgersen | 39.5 | 17.4 | 186.0 | 3800.0 | Female |
| 2 | Adelie | Torgersen | 40.3 | 18.0 | 195.0 | 3250.0 | Female |
| 3 | Adelie | Torgersen | NaN | NaN | NaN | NaN | NaN |
| 4 | Adelie | Torgersen | 36.7 | 19.3 | 193.0 | 3450.0 | Female |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 339 | Gentoo | Biscoe | NaN | NaN | NaN | NaN | NaN |
| 340 | Gentoo | Biscoe | 46.8 | 14.3 | 215.0 | 4850.0 | Female |
| 341 | Gentoo | Biscoe | 50.4 | 15.7 | 222.0 | 5750.0 | Male |
| 342 | Gentoo | Biscoe | 45.2 | 14.8 | 212.0 | 5200.0 | Female |
| 343 | Gentoo | Biscoe | 49.9 | 16.1 | 213.0 | 5400.0 | Male |
344 rows × 7 columns
tips
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 239 | 29.03 | 5.92 | Male | No | Sat | Dinner | 3 |
| 240 | 27.18 | 2.00 | Female | Yes | Sat | Dinner | 2 |
| 241 | 22.67 | 2.00 | Male | Yes | Sat | Dinner | 2 |
| 242 | 17.82 | 1.75 | Male | No | Sat | Dinner | 2 |
| 243 | 18.78 | 3.00 | Female | No | Thur | Dinner | 2 |
244 rows × 7 columns
sns.set_theme()
sns.scatterplot(data=tips, x="total_bill", y="tip")
<Axes: xlabel='total_bill', ylabel='tip'>
sns.scatterplot(data=tips, x="total_bill", y="tip", hue="smoker")
<Axes: xlabel='total_bill', ylabel='tip'>
sns.scatterplot(data=tips, x="total_bill", y="tip", hue="sex")
<Axes: xlabel='total_bill', ylabel='tip'>
sns.scatterplot(data=tips, x="total_bill", y="tip", hue="day")
<Axes: xlabel='total_bill', ylabel='tip'>
sns.scatterplot(data=tips, x="total_bill", y="tip", hue="sex",style="time")
<Axes: xlabel='total_bill', ylabel='tip'>
sns.scatterplot(data=tips, x="total_bill", y="tip", hue="sex",style="sex")
<Axes: xlabel='total_bill', ylabel='tip'>
sns.scatterplot(data=tips, x="total_bill", y="tip", hue="sex",size="size")
<Axes: xlabel='total_bill', ylabel='tip'>
flights = sns.load_dataset("flights")
flights
| year | month | passengers | |
|---|---|---|---|
| 0 | 1949 | Jan | 112 |
| 1 | 1949 | Feb | 118 |
| 2 | 1949 | Mar | 132 |
| 3 | 1949 | Apr | 129 |
| 4 | 1949 | May | 121 |
| ... | ... | ... | ... |
| 139 | 1960 | Aug | 606 |
| 140 | 1960 | Sep | 508 |
| 141 | 1960 | Oct | 461 |
| 142 | 1960 | Nov | 390 |
| 143 | 1960 | Dec | 432 |
144 rows × 3 columns
# Here the plot is plotting the average passengers
sns.lineplot(data=flights, x="year", y="passengers")
<Axes: xlabel='year', ylabel='passengers'>
flights[flights['year'] == 1950]
| year | month | passengers | |
|---|---|---|---|
| 12 | 1950 | Jan | 115 |
| 13 | 1950 | Feb | 126 |
| 14 | 1950 | Mar | 141 |
| 15 | 1950 | Apr | 135 |
| 16 | 1950 | May | 125 |
| 17 | 1950 | Jun | 149 |
| 18 | 1950 | Jul | 170 |
| 19 | 1950 | Aug | 170 |
| 20 | 1950 | Sep | 158 |
| 21 | 1950 | Oct | 133 |
| 22 | 1950 | Nov | 114 |
| 23 | 1950 | Dec | 140 |
flights.passengers.sum()
40363
# estimator plots the sum of passengers
sns.lineplot(data=flights, x="year", y="passengers",estimator=sum)
<Axes: xlabel='year', ylabel='passengers'>
sns.lineplot(data=flights, x="year", y="passengers",estimator=max)
<Axes: xlabel='year', ylabel='passengers'>
sns.lineplot(data=flights,x="year",y="passengers",hue="month")
<Axes: xlabel='year', ylabel='passengers'>
trips = sns.load_dataset("taxis",parse_dates=["pickup","dropoff"])
trips
| pickup | dropoff | passengers | distance | fare | tip | tolls | total | color | payment | pickup_zone | dropoff_zone | pickup_borough | dropoff_borough | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-03-23 20:21:09 | 2019-03-23 20:27:24 | 1 | 1.60 | 7.0 | 2.15 | 0.0 | 12.95 | yellow | credit card | Lenox Hill West | UN/Turtle Bay South | Manhattan | Manhattan |
| 1 | 2019-03-04 16:11:55 | 2019-03-04 16:19:00 | 1 | 0.79 | 5.0 | 0.00 | 0.0 | 9.30 | yellow | cash | Upper West Side South | Upper West Side South | Manhattan | Manhattan |
| 2 | 2019-03-27 17:53:01 | 2019-03-27 18:00:25 | 1 | 1.37 | 7.5 | 2.36 | 0.0 | 14.16 | yellow | credit card | Alphabet City | West Village | Manhattan | Manhattan |
| 3 | 2019-03-10 01:23:59 | 2019-03-10 01:49:51 | 1 | 7.70 | 27.0 | 6.15 | 0.0 | 36.95 | yellow | credit card | Hudson Sq | Yorkville West | Manhattan | Manhattan |
| 4 | 2019-03-30 13:27:42 | 2019-03-30 13:37:14 | 3 | 2.16 | 9.0 | 1.10 | 0.0 | 13.40 | yellow | credit card | Midtown East | Yorkville West | Manhattan | Manhattan |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6428 | 2019-03-31 09:51:53 | 2019-03-31 09:55:27 | 1 | 0.75 | 4.5 | 1.06 | 0.0 | 6.36 | green | credit card | East Harlem North | Central Harlem North | Manhattan | Manhattan |
| 6429 | 2019-03-31 17:38:00 | 2019-03-31 18:34:23 | 1 | 18.74 | 58.0 | 0.00 | 0.0 | 58.80 | green | credit card | Jamaica | East Concourse/Concourse Village | Queens | Bronx |
| 6430 | 2019-03-23 22:55:18 | 2019-03-23 23:14:25 | 1 | 4.14 | 16.0 | 0.00 | 0.0 | 17.30 | green | cash | Crown Heights North | Bushwick North | Brooklyn | Brooklyn |
| 6431 | 2019-03-04 10:09:25 | 2019-03-04 10:14:29 | 1 | 1.12 | 6.0 | 0.00 | 0.0 | 6.80 | green | credit card | East New York | East Flatbush/Remsen Village | Brooklyn | Brooklyn |
| 6432 | 2019-03-13 19:31:22 | 2019-03-13 19:48:02 | 1 | 3.85 | 15.0 | 3.36 | 0.0 | 20.16 | green | credit card | Boerum Hill | Windsor Terrace | Brooklyn | Brooklyn |
6433 rows × 14 columns
trips['hour'] = trips['pickup'].dt.hour
trips[['hour']]
| hour | |
|---|---|
| 0 | 20 |
| 1 | 16 |
| 2 | 17 |
| 3 | 1 |
| 4 | 13 |
| ... | ... |
| 6428 | 9 |
| 6429 | 17 |
| 6430 | 22 |
| 6431 | 10 |
| 6432 | 19 |
6433 rows × 1 columns
# plotting mean payment done in cash or credit card
sns.lineplot(data=trips,x='hour',y='total',hue="payment",style="color",ci=None)
C:\Users\SUDIPT PC\AppData\Local\Temp\ipykernel_32480\2583105076.py:2: FutureWarning: The `ci` parameter is deprecated. Use `errorbar=None` for the same effect. sns.lineplot(data=trips,x='hour',y='total',hue="payment",style="color",ci=None)
<Axes: xlabel='hour', ylabel='total'>
sns.lineplot(data=trips,x='hour',y='total',hue="payment",style="color",size="passengers",ci=None)
C:\Users\SUDIPT PC\AppData\Local\Temp\ipykernel_32480\2028404596.py:1: FutureWarning: The `ci` parameter is deprecated. Use `errorbar=None` for the same effect. sns.lineplot(data=trips,x='hour',y='total',hue="payment",style="color",size="passengers",ci=None)
<Axes: xlabel='hour', ylabel='total'>
sns.relplot(data=tips, x="total_bill", y="tip", col="sex")
<seaborn.axisgrid.FacetGrid at 0x2883700d360>
sns.relplot(data=tips, x="total_bill", y="tip", hue="smoker", col="sex")
<seaborn.axisgrid.FacetGrid at 0x28837269840>
sns.relplot(data=tips, x="total_bill", y="tip", hue="smoker", col="sex", row="time")
<seaborn.axisgrid.FacetGrid at 0x288370b9db0>
sns.relplot(data=trips, x="hour",y="total",kind="line",hue="payment")
<seaborn.axisgrid.FacetGrid at 0x2883c2bf490>
sns.relplot(data=trips, x="hour",
y="total",kind="line",
col="pickup_borough",
hue="payment")
<seaborn.axisgrid.FacetGrid at 0x2883c1c4730>
sns.relplot(data=trips, x="hour",
y="total",kind="line",
col="pickup_borough",
hue="payment",row="dropoff_borough")
<seaborn.axisgrid.FacetGrid at 0x28837917a30>
sns.relplot(data=trips, x="hour",
y="total",kind="line",
col="pickup_borough",
hue="payment",height=4)
<seaborn.axisgrid.FacetGrid at 0x28843f91bd0>
sns.relplot(data=tips, x="total_bill", y="tip", hue="smoker",col="sex",row="time",height=5,aspect=1.5)
<seaborn.axisgrid.FacetGrid at 0x1433e353be0>
sns.histplot(data=tips,x="tip")
<Axes: xlabel='tip', ylabel='Count'>
sns.histplot(data=tips,x="tip",hue="time")
<Axes: xlabel='tip', ylabel='Count'>
sns.histplot(data=tips,x="tip",hue="smoker",multiple="stack")
<Axes: xlabel='tip', ylabel='Count'>
sns.histplot(data=tips,x="tip",hue="smoker",multiple="dodge")
<Axes: xlabel='tip', ylabel='Count'>
sns.histplot(data=penguins,x="body_mass_g",bins=30,binwidth=100,hue="species",multiple="stack")
<Axes: xlabel='body_mass_g', ylabel='Count'>
sns.histplot(data=penguins,x="body_mass_g",bins=30,binwidth=100,hue="species",multiple="stack",element="step")
<Axes: xlabel='body_mass_g', ylabel='Count'>
sns.histplot(data=penguins,x="body_mass_g",bins=30,binwidth=100,hue="species",multiple="stack",element="step",kde=True)
<Axes: xlabel='body_mass_g', ylabel='Count'>
sns.kdeplot(data=penguins,x="body_mass_g",hue="species")
<Axes: xlabel='body_mass_g', ylabel='Density'>
sns.kdeplot(data=penguins,x="body_mass_g",hue="species",bw_adjust=0.2)
<Axes: xlabel='body_mass_g', ylabel='Density'>
sns.kdeplot(data=penguins,x="body_mass_g",hue="species",multiple="stack")
<Axes: xlabel='body_mass_g', ylabel='Density'>
sns.histplot(data=penguins,x="body_mass_g")
<Axes: xlabel='body_mass_g', ylabel='Count'>
sns.histplot(data=penguins,x="flipper_length_mm")
<Axes: xlabel='flipper_length_mm', ylabel='Count'>
sns.histplot(data=penguins,x="body_mass_g",y="flipper_length_mm")
<Axes: xlabel='body_mass_g', ylabel='flipper_length_mm'>
sns.kdeplot(data=penguins,x="body_mass_g",y="flipper_length_mm")
<Axes: xlabel='body_mass_g', ylabel='flipper_length_mm'>
sns.kdeplot(data=penguins,x="body_mass_g",y="flipper_length_mm",hue="species")
<Axes: xlabel='body_mass_g', ylabel='flipper_length_mm'>
sns.kdeplot(data=penguins,x="bill_length_mm",y="flipper_length_mm",hue="species")
<Axes: xlabel='bill_length_mm', ylabel='flipper_length_mm'>
sns.histplot(data=penguins,x="bill_length_mm",y="flipper_length_mm",hue="species")
<Axes: xlabel='bill_length_mm', ylabel='flipper_length_mm'>
sns.rugplot(data=tips,x="tip",height=0.5)
<Axes: xlabel='tip'>
sns.rugplot(data=tips,y="tip",height=0.8)
<Axes: ylabel='tip'>
sns.kdeplot(data=tips,x="total_bill")
sns.rugplot(data=tips,x="total_bill",height=0.05)
<Axes: xlabel='total_bill', ylabel='Density'>
sns.scatterplot(data=tips,x="total_bill",y="tip")
sns.rugplot(data=tips,x="total_bill",y="tip")
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[69], line 1 ----> 1 sns.scatterplot(data=tips,x="total_bill",y="tip") 2 sns.rugplot(data=tips,x="total_bill",y="tip") NameError: name 'tips' is not defined
penguins
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | |
|---|---|---|---|---|---|---|---|
| 0 | Adelie | Torgersen | 39.1 | 18.7 | 181.0 | 3750.0 | Male |
| 1 | Adelie | Torgersen | 39.5 | 17.4 | 186.0 | 3800.0 | Female |
| 2 | Adelie | Torgersen | 40.3 | 18.0 | 195.0 | 3250.0 | Female |
| 3 | Adelie | Torgersen | NaN | NaN | NaN | NaN | NaN |
| 4 | Adelie | Torgersen | 36.7 | 19.3 | 193.0 | 3450.0 | Female |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 339 | Gentoo | Biscoe | NaN | NaN | NaN | NaN | NaN |
| 340 | Gentoo | Biscoe | 46.8 | 14.3 | 215.0 | 4850.0 | Female |
| 341 | Gentoo | Biscoe | 50.4 | 15.7 | 222.0 | 5750.0 | Male |
| 342 | Gentoo | Biscoe | 45.2 | 14.8 | 212.0 | 5200.0 | Female |
| 343 | Gentoo | Biscoe | 49.9 | 16.1 | 213.0 | 5400.0 | Male |
344 rows × 7 columns
sns.ecdfplot(data=penguins,x="body_mass_g",hue="species")
<Axes: xlabel='body_mass_g', ylabel='Proportion'>
sns.displot(kind="hist",data=penguins,x="body_mass_g",height=4,col="species",hue="sex",row="island")
<seaborn.axisgrid.FacetGrid at 0x28860f8c190>
sns.displot(kind="hist",data=penguins,x="body_mass_g",height=4,col="species",hue="sex",multiple="dodge")
<seaborn.axisgrid.FacetGrid at 0x2885de1f0a0>
sns.displot(kind="hist",data=penguins,x="body_mass_g",height=4,col="species",hue="sex",element="step")
<seaborn.axisgrid.FacetGrid at 0x288359a3010>
sns.displot(data=tips,kind="kde",x="total_bill",y="tip",rug=True)
<seaborn.axisgrid.FacetGrid at 0x2883595b070>
penguins
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | |
|---|---|---|---|---|---|---|---|
| 0 | Adelie | Torgersen | 39.1 | 18.7 | 181.0 | 3750.0 | Male |
| 1 | Adelie | Torgersen | 39.5 | 17.4 | 186.0 | 3800.0 | Female |
| 2 | Adelie | Torgersen | 40.3 | 18.0 | 195.0 | 3250.0 | Female |
| 3 | Adelie | Torgersen | NaN | NaN | NaN | NaN | NaN |
| 4 | Adelie | Torgersen | 36.7 | 19.3 | 193.0 | 3450.0 | Female |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 339 | Gentoo | Biscoe | NaN | NaN | NaN | NaN | NaN |
| 340 | Gentoo | Biscoe | 46.8 | 14.3 | 215.0 | 4850.0 | Female |
| 341 | Gentoo | Biscoe | 50.4 | 15.7 | 222.0 | 5750.0 | Male |
| 342 | Gentoo | Biscoe | 45.2 | 14.8 | 212.0 | 5200.0 | Female |
| 343 | Gentoo | Biscoe | 49.9 | 16.1 | 213.0 | 5400.0 | Male |
344 rows × 7 columns
sns.countplot(data=penguins,x="species")
<Axes: xlabel='species', ylabel='count'>
penguins["species"].value_counts().plot(kind='bar',color=["red","yellow","magenta"])
<Axes: >
sns.countplot(data=penguins,x="species",hue="sex")
<Axes: xlabel='species', ylabel='count'>
titanic = pd.read_csv("titanic.csv")
sns.countplot(data=titanic,y="pclass",hue="sex")
<Axes: xlabel='count', ylabel='pclass'>
trips_df = trips.dropna()
my_palette = ["red","green","magenta","yellow"]
sns.catplot(kind="strip",data=trips_df,x="pickup_borough",y="distance",palette=my_palette,height=4,aspect=2.5)
C:\Users\SUDIPT PC\AppData\Local\Temp\ipykernel_12580\2751856868.py:2: FutureWarning: Passing `palette` without assigning `hue` is deprecated. sns.catplot(kind="strip",data=trips_df,x="pickup_borough",y="distance",palette=my_palette,height=4,aspect=2.5)
<seaborn.axisgrid.FacetGrid at 0x143362a0040>
trips = sns.load_dataset("taxis",parse_dates=["pickup","dropoff"])
trips_sample = trips.nlargest(600,"total").dropna()
trips_sample
| pickup | dropoff | passengers | distance | fare | tip | tolls | total | color | payment | pickup_zone | dropoff_zone | pickup_borough | dropoff_borough | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5364 | 2019-03-17 16:59:17 | 2019-03-17 18:04:08 | 2 | 36.70 | 150.00 | 0.00 | 24.02 | 174.82 | yellow | cash | JFK Airport | JFK Airport | Queens | Queens |
| 4218 | 2019-03-29 17:32:20 | 2019-03-29 18:53:52 | 1 | 26.92 | 75.50 | 23.19 | 0.00 | 100.49 | yellow | credit card | JFK Airport | Cobble Hill | Queens | Brooklyn |
| 5567 | 2019-03-07 00:28:57 | 2019-03-07 02:02:55 | 1 | 25.51 | 93.50 | 0.00 | 0.00 | 94.80 | green | credit card | Sunset Park West | Saint Albans | Brooklyn | Queens |
| 5827 | 2019-03-05 10:34:36 | 2019-03-05 11:44:01 | 1 | 20.64 | 86.14 | 0.00 | 5.76 | 92.40 | green | credit card | Coney Island | Upper East Side North | Brooklyn | Manhattan |
| 2387 | 2019-03-28 15:58:52 | 2019-03-28 15:59:25 | 1 | 1.80 | 69.06 | 20.80 | 0.00 | 90.16 | yellow | credit card | JFK Airport | JFK Airport | Queens | Queens |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2186 | 2019-03-05 23:20:47 | 2019-03-05 23:43:48 | 3 | 7.84 | 25.00 | 0.00 | 5.76 | 34.56 | yellow | cash | Jackson Heights | Clinton East | Queens | Manhattan |
| 2686 | 2019-03-11 10:42:37 | 2019-03-11 11:11:12 | 1 | 7.30 | 25.50 | 5.76 | 0.00 | 34.56 | yellow | credit card | Upper West Side South | Financial District North | Manhattan | Manhattan |
| 3139 | 2019-03-17 12:53:40 | 2019-03-17 13:35:57 | 1 | 3.44 | 25.50 | 5.76 | 0.00 | 34.56 | yellow | credit card | Garment District | Upper East Side North | Manhattan | Manhattan |
| 3857 | 2019-03-30 08:29:43 | 2019-03-30 08:44:54 | 1 | 7.68 | 23.00 | 5.00 | 5.76 | 34.56 | yellow | credit card | East Harlem South | LaGuardia Airport | Manhattan | Queens |
| 5425 | 2019-03-28 13:03:43 | 2019-03-28 13:32:27 | 1 | 7.40 | 25.50 | 5.76 | 0.00 | 34.56 | yellow | credit card | Battery Park City | Midtown East | Manhattan | Manhattan |
577 rows × 14 columns
colors = ['orange','red','green','purple']
sns.catplot(data=trips_sample,kind="swarm",x="pickup_borough",y="total",palette=colors,height=5,aspect=2)
C:\Users\SUDIPT PC\AppData\Local\Temp\ipykernel_12580\4274078373.py:2: FutureWarning: Passing `palette` without assigning `hue` is deprecated. sns.catplot(data=trips_sample,kind="swarm",x="pickup_borough",y="total",palette=colors,height=5,aspect=2)
<seaborn.axisgrid.FacetGrid at 0x143366e1ff0>
C:\Users\SUDIPT PC\anaconda3\lib\site-packages\seaborn\categorical.py:3544: UserWarning: 6.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
titanic
| pclass | survived | name | sex | age | sibsp | parch | ticket | fare | cabin | embarked | boat | body | home.dest | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | Allen, Miss. Elisabeth Walton | female | 29 | 0 | 0 | 24160 | 211.3375 | B5 | S | 2 | ? | St Louis, MO |
| 1 | 1 | 1 | Allison, Master. Hudson Trevor | male | 0.9167 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | 11 | ? | Montreal, PQ / Chesterville, ON |
| 2 | 1 | 0 | Allison, Miss. Helen Loraine | female | 2 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | ? | ? | Montreal, PQ / Chesterville, ON |
| 3 | 1 | 0 | Allison, Mr. Hudson Joshua Creighton | male | 30 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | ? | 135 | Montreal, PQ / Chesterville, ON |
| 4 | 1 | 0 | Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | female | 25 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | ? | ? | Montreal, PQ / Chesterville, ON |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1304 | 3 | 0 | Zabour, Miss. Hileni | female | 14.5 | 1 | 0 | 2665 | 14.4542 | ? | C | ? | 328 | ? |
| 1305 | 3 | 0 | Zabour, Miss. Thamine | female | ? | 1 | 0 | 2665 | 14.4542 | ? | C | ? | ? | ? |
| 1306 | 3 | 0 | Zakarian, Mr. Mapriededer | male | 26.5 | 0 | 0 | 2656 | 7.225 | ? | C | ? | 304 | ? |
| 1307 | 3 | 0 | Zakarian, Mr. Ortin | male | 27 | 0 | 0 | 2670 | 7.225 | ? | C | ? | ? | ? |
| 1308 | 3 | 0 | Zimmerman, Mr. Leo | male | 29 | 0 | 0 | 315082 | 7.875 | ? | S | ? | ? | ? |
1309 rows × 14 columns